Plurrrr

If you’re managing a complex git codebase with multiple developers, then you may well be using a tool like GitHub or BitBucket to delve into the history and figure out branch and merge issues.

These GUIs are great for providing a nice user interface for managing pull requests and simple histories and the like, but when the workflow SHTF there’s no substitute for using git log and its relatively little-known flags to really dig into the situation.

I recently got to use a very curious Haskell technique in production: time traveling. I say this with the utmost seriousness. This technique worked like magic for the problem I was trying to solve, and so I thought I’d share what I learned. In addition to the technique and its workings, I will also explain how time traveling can be misused, yielding computations that never terminate.

This week's blog post is about solving the "Task Ordering" problem in Python. You will learn:

• How to solve the problem using brute force.
• How to use Khan's Algorithm to solve the problem more efficiently.

At their core, neural networks are functions. They take some input, perform a series of computations, and produce an output. Though most networks operate in the realm of vectors and matrices, it can be a useful exercise to see them without the extra barrier of linear algebra. For this purposes of this explanation, we will only cover single variable functions, but the principles we will see can be extended into any number of dimensions.